DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhou (US 20220027602 A1) and in view of Abreu (US 20160196665 A1), and further in view of Zhou (US 20170213359 A1; hereafter ’359).
Regarding to claim 1, Zhou discloses a method for generating digital human facial expressions (Fig. 1; [0016]: use face shape coefficients to control a face shape of the 3D face model and use facial expression coefficients to control a facial expression of a 3D human face mode; enhance the accuracy of initial expression and individual expressions; [0048]: perform a forward propagation to generate a set of face shape coefficients and a set of facial expression coefficients; generate the face shape coefficients and expression coefficients of the 3D deformed model 102; [0071]: generate the 3D model of the user using the 3D morphable model 102 according to the set of corresponding face shape coefficients and the set of expression coefficients), comprising:
capturing a performer's facial expression images ([0016]: receive multiple 2D face images; Fig. 2; [0020]: substantially simultaneously capture images of the reference user R from different angles; capture color images and depth map of the face of the reference user R; capture a plurality of auxiliary color images Is(1) to Is(18) of the reference user R from the plurality of side angles;[0035]: perform the ground truth migration for the viewing angle of the auxiliary color came);
selecting a plurality of images from the facial expression images ([0016]: receive 2D face images; avatar animation driving; [0027]: pre-process the selected images of large poses; [0047]: crop the selected training images; [0048]: one image is selected from the 3D front angle image and the plurality of 3D side angle images of the 3D GT model 104) and fitting each selected image by using an active appearance model to obtain a plurality of facial marks (Fig. 1; [0016]: employ the reconstructed face to obtain 3D landmark points; adopt a plurality of model coefficients to generate facial landmarks of the 3D face model; the 3D facial reconstruction system fits a 3D morphable model (3DMM) to a 2D face image to reconstruct a 3D face model; [0024]: the processor 10 generates a plurality of 3D side angle images of the 3D GT model 104 according to the 3D front angle image and the plurality of auxiliary color images; [0031]: the processor 10 performs optimal fitting on the main color image Ip and the main depth map Dp to generate a pose, a set of front-angle face shape coefficients and a set of front-angle facial expression coefficients; Fig. 8; [0047]: the processor 10 performs a face detection on the training image, then detects 2D landmark points of the face);
calculating values of controllers for driving expressions of 3D model of a digital human, based on the plurality of facial marks obtained from each selected frame and a pre-determined mapping relationship from facial expressions to the 3D model (Fig. 1; [0016]: use face shape coefficients, i.e. controller values, to control a face shape of the 3D face model and use facial expression coefficients, i.e. controller values, to control a facial expression of a 3D human face mode; enhance the accuracy of initial expression and individual expressions; the artificial neural network model trains face shape coefficients and facial expression coefficients respectively to enhance the accuracy of initial expression and individual expressions; [0031]: the processor 10 performs optimal fitting on the main color image Ip and the main depth map Dp to generate a pose, a set of front-angle face shape coefficients and a set of front-angle facial expression coefficients; [0033]: the processor 10 performs landmark detection on the color image Ip, and then employs the main depth map Dp to perform an optimal fitting to obtain the pose, the set of front-angle face shape coefficients, and the set of front-angle facial expression coefficients; [0048]: perform a forward propagation to generate a set of face shape coefficients and a set of facial expression coefficients; generate the face shape coefficients and expression coefficients of the 3D deformed model 102);
Zhou fails to explicitly disclose:
images are video and video frames;
wherein determining the active appearance model comprises the following steps:
training a shape model and a texture model based on a plurality of facial marks annotated in a training set, and
obtaining a regression matrix through perturbation experiments, wherein the regression matrix represents the relationship between parameter variations obtained from the perturbation experiments and texture residual(s), and the training set comprises a plurality of images containing facial expressions.
In same field of endeavor, Abreu teaches:
images are video and video frames ([0115]: the camera captures a (video) image of the user; [0129]: receive captured image data from the camera, which can be an image in a sequence of images or video frames);
wherein determining the active appearance model ([0004]: Active Appearance Modelling; Fig. 1; [0066]: the texture model training module 4; [0069]: produce a particular desired virtual look or appearance; Fig. 1; [0079]: shape model training module; [0082]: an appearance sub-shape module) comprises the following steps:
training a shape model and a texture model based on a plurality of facial marks annotated in a training set (Fig. 1; [0061]: generate and store trained texture models 30; generate and store trained shape models for use during real-time processing of input image data from a camera 9 by a tracking module 11; [0065]: the texture model training module 4 uses the same set of labelled feature points as the tracking module 11; [0066]: the texture model training module 4 may be configured to subsequently perform triangulation to generate a mesh of triangular regions based on the labelled feature points), and
obtaining a regression matrix through perturbation experiments ([0010]: a highlight adjustment, a color adjustment, a glitter adjustment, a lighting model adjustment, a blend color adjustment, and an alpha blend adjustment; [0084]: compute the respective regression coefficient matrices 45, 47 based on any known regression analysis technique, such as principal component regression (PCR), linear regression, least squares, etc; [0099]: Tthe regression computation module 43 computes the regression coefficient matrices 45, 47 based on feature point descriptors and corresponding offsets that are determined from the training images in the database; [0131]: the refinement module 61 determines a plurality of candidate sub-shapes from the current adjusted global shape, based on the sub-shape models 29), and the training set comprises a plurality of images containing facial expressions ([0029]: the selected facial feature may be the user's lips; a representation of the lips may be stored; [0031]: change the appearance of the user's facial features; [0081]: the training images 23 include subject faces and facial features in different orientations and variations, such as front-on, slightly to one side, closed, pressed, open slightly, open wide, etc; Fig. 1; [0087]: the model training module retrieves a first one of the plurality of user-defined masks 14a from the image database; [0090]: define a plurality of labelled feature points 25 in the training images 23 of the training image database).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhou to include images are video and video frames; wherein determining the active appearance model comprises the following steps: training a shape model and a texture model based on a plurality of facial marks annotated in a training set, and obtaining a regression matrix through perturbation experiments, and the training set comprises a plurality of images containing facial expressions as taught by Abreu. The motivation for doing so would have been to generate and store trained texture models; to generate and store trained shape models for use during real-time processing of input image data from a camera by a tracking module; to define a plurality of labelled feature points in the training images of the training image database; to improve the robustness of initial positioning of a detected face within a bounding box as taught by Abreu in paragraphs [0061], [0090], and [0096].
Zhou in view of Abreu fails to explicitly disclose:
wherein the regression matrix represents the relationship between parameter variations obtained from the perturbation experiments and texture residual(s).
In same field of endeavor, Zhou (’359) teaches:
wherein the regression matrix represents the relationship between parameter variations obtained from the perturbation experiments and texture residual(s) ([0015]: determine the regression factor matrix by minimizing an error between the extent to which the second form coefficient is updated and an extent to which a third form coefficient is updated, calculated based on the nonlinear feature; [0022]: obtaining the regression factor matrix based on the nonlinear feature of the training sample and an extent to which a second form coefficient of the training sample is updated; Fig. 2A; [0052]: a statistical texture model is trained using the human face image in which the feature point is represented as the training sample such that operations 211 through 213 are selectively performed; a linear form coefficient regression factor matrix is calculated based on the nonlinear feature and an extent to which a form model coefficient is updated, an extent to which a form coefficient is updated is calculated based on the previously obtained linear form coefficient regression factor matrix and the nonlinear feature; terminate when the form coefficient converges; [0056-0058]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of claimed invention to modify Zhou in view of Abreu to include wherein the regression matrix represents the relationship between parameter variations obtained from the perturbation experiments and texture residual(s) as taught by Zhou (’359). The motivation for doing so would have been to determine the regression factor matrix based on the nonlinear feature and an extent to which a second form coefficient of a training sample is updated; to determine the regression factor matrix by minimizing an error between the extent to which the second form coefficient is updated and an extent to which a third form coefficient is updated as taught by Zhou (’359) in paragraphs [0012] and [0015].
Regarding to claim 2, Zhou in view of Abreu and Zhou (’359) discloses the method for generating digital human facial expressions according to claim 1, wherein determining the mapping relationship from the facial expressions to the 3D model (Zhou; Fig. 1; [0016]: use face shape coefficients to control a face shape of the 3D face model and use facial expression coefficients to control a facial expression of a 3D human face model) comprises:
adjusting the values of the controllers for each facial expression in the training set to obtain a corresponding expression of the digital human with high similarity (Zhou (‘359); [0017]: a regression factor matrix is obtained through training; Fig. 2B; [0064]: as in the images 203 through 206, a form coefficient is iteratively updated until the form coefficient converges using Equation 4;
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; [0070]: a form of a predetermined or, alternatively, desired human face is generated and the global similarity transformation is additionally performed; [0076]: perform global similarity transformation of a fixed form);
determining the mapping relationship from the facial expressions to the 3D model based on the adjusted values of the controllers and coordinates of the annotated facial marks in the training set (Zhou; [0026]: perform high-precision expression fitting to generate an accurate 3D front angle image; [0028]: utilize the main depth map Dp to perform high-precision expression fitting to generate the accurate 3D front angle image; [0031]: the processor 10 performs optimal fitting on the main color image Ip and the main depth map Dp to generate a pose, a set of front-angle face shape coefficients and a set of front-angle facial expression coefficients; Fig. 5; [0035]: generate accurate 3D side angle images).
Same motivation of claim 1 is applied here.
Regarding to claim 3, Zhou in view of Abreu and Zhou (’359) discloses the method for generating digital human facial expressions according to claim 1, wherein the step of fitting each selected frame by using the active appearance model to obtain the plurality of facial marks (same as rejected in claim 1) comprises:
(1) determining texture features based on predetermined initialization reference marks (Abreu; [0070]: identify the location of the texture portion relative to the pixel location of a captured image; [0139]: each retrieved texture model is selected based on a corresponding set of colourisation parameters; a first mask 14b-1 defines a masked lip region of the reference image 8 and is associated with a first optimized mesh 18-1 defining polygonal areas around the masked lip region);
(2) calculating difference between the texture features and the average texture features as texture residual(s), and adjusting the parameters of the texture model to obtain new average texture features (Zhou (‘359); [0013]: learn an average texture feature from the training sample and determine a value of a difference between the average texture feature and a nonlinear texture feature extracted from the training sample as a nonlinear feature of the training sample; [0052]: an intermediate form and an average form were updated in a previous iteration; output regression factor matrix, a form model, and an average texture);
(3) determining a parameter variation matrix based on the regression matrix and the texture residual(s) to derive shape parameters and new texture features (Zhou (‘359); [0015]: determine the regression factor matrix by minimizing an error between the extent to which the second form coefficient is updated and an extent to which a third form coefficient is updated, calculated based on the nonlinear feature; [0016]: obtain a regression factor matrix R.sub.k used for a k-th iterative update of the third form coefficient using a target function; Fig. 2B; [0064]: as in the images 203 through 206, a form coefficient is iteratively updated until the form coefficient converges using Equation 4;
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),
iterating steps (2) and (3) until the texture residual(s) exceed a predetermined threshold or the maximum number of iterations is reached (or is optional; Zhou (‘359); [0016]: a k-th iterative update of the third form coefficient using a target function; [0017]: iteratively update a first form coefficient based on the nonlinear feature and a regression factor matrix obtained through training; [0018]: obtain a similarity transformation matrix by arranging an intermediate form and an average form obtained through an update; [0052]: Operations 215 through 219 are iteratively performed and then terminate when the form coefficient converges or operations 215 through 219 terminate when they have been performed a predetermined or, alternatively, desired number of times).
Same motivation of claim 1 is applied here.
Regarding to claim 4, Zhou in view of Abreu and Zhou (’359) discloses the method for generating digital human facial expressions according to claim 3, wherein the parameters of the texture model are adjusted using the following equation of the texture model:
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where g is the texture feature, g is the average texture feature, Φ.sub.g is the basis vector of texture feature space, and b.sub.g is the parameter of the texture model, represented by eigenvalue of the texture feature model (Zhou (‘359); [0056-0057]:
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; s.sub.0 denotes an average form, p.sub.i denotes a local form coefficient, and s.sub.i denotes a basic factor).
Same motivation of claim 1 is applied here.
Regarding to claim 5, Zhou in view of Abreu and Zhou (’359) discloses the method for generating digital human facial expressions according to claim 1, wherein the step of calculating the values of the controllers for driving expressions of the 3D model of the digital human comprises substituting the coordinates of the plurality of facial marks into the following equation to obtain the values of the controllers:
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where φ is a basis function, φ.sub.ji=φ(∥x.sub.j−x.sub.i∥), x represents the coordinates of the plurality of facial marks, N is the number of selected frames, and y represents the values of the controllers (Zhou (‘359); [0015]: determine the regression factor matrix by minimizing an error between the extent to which the second form coefficient is updated and an extent to which a third form coefficient is updated, calculated based on the nonlinear feature; [0016]: obtain a regression factor matrix R; [0018]: obtain a similarity transformation matrix by arranging an intermediate form and an average form obtained through an update; Fig. 2B; [0064]: as in the images 203 through 206, a form coefficient is iteratively updated until the form coefficient converges using Equation 4;
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; [0078-0079]:
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; [0081-0083]:
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);
the weights ware pre-calculated using the equation based on the annotated facial marks in the training set, as the mapping relationship from the facial expressions to the 3D model (Zhou (‘359); [0007]: iteratively update a first form coefficient based on, a nonlinear feature extracted from an image, and a regression factor matrix obtained through training; [0015]: determine the regression factor matrix by minimizing an error between the extent to which the second form coefficient is updated and an extent to which a third form coefficient is updated, calculated based on the nonlinear feature; Fig. 2B; [0064]: as in the images 203 through 206, a form coefficient is iteratively updated until the form coefficient converges using Equation 4;
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).
Same motivation of claim 1 is applied here.
Regarding to claim 6, Zhou in view of Abreu and Zhou (’359) discloses the method for generating digital human facial expressions according to claim 1, wherein the perturbation experiments comprise variations in perturbation values of scaling (Abreu; [0067]: a different scale from the texture image data; [0097]: the calculated coordinates of the relative position and the relative scale are stored for example in the training image database), perturbation values of rotation angle (Abreu; [0104]: rotation and scaling; scale and rotation), perturbation values of translation (Abreu; [0143]: translation in the two-dimensional plane), perturbation values of shape model parameter (Abreu; [0084]: generate a global shape regression coefficient matrix 45 based on the global shape 27 generated by the shape model module 21; [0104]: once the initial scaled mean shape has been modified by the refinement process, scale and rotation will be of great importance.), and perturbation values of texture model parameter (Abreu; [0065]: optimize texture space across all areas of the face where virtual makeup may be applied; [0067]: the normalised mesh is defined at a different scale from the texture image data; [0068]: generate a plurality of optimized texture models 14, based on the normalised mesh 10 retrieved from the object model database; [0076]: perform an alpha blend of the respective layers of associated regions of warped texture data).
Same motivation of claim 1 is applied here.
Regarding to claim 7, Zhou in view of Abreu and Zhou (’359) discloses the method for generating digital human facial expressions according to claim 1, wherein the plurality of images contained in the training set are selected as a plurality of keyframes from a pre-obtained facial expression video (Abreu; [0027]: a plurality of training images; [0090]: the shape model training module processes user input to define a plurality of labelled feature points 25 in the training images 23 of the training image database; [0091]: determine a global shape model 27 for the trained face model 31, based on the training images 23 and associated feature points 25 retrieves from the training image database; Fig. 15; [0115]: the camera captures a video images of the user; Fig. 15; [0129]: a sequence of images or video frames; the tracking module determines if an object, a subject's face in this exemplary embodiment, was previously detected and located for tracking in a prior image or video frame).
Same motivation of claim 1 is applied here.
Regarding to claim 8, Zhou in view of Abreu and Zhou (’359) discloses the method for generating a digital human facial expression according to claim 2, wherein training the shape model comprises aligning the coordinates of the facial marks in the training set with average reference marks and performing principal component analysis on the transformed training set to obtain the shape model (Zhou; Fig. 1; [0016]: the 3D morphable model may be based on the principal component analysis (PCA), and may adopt a plurality of model coefficients to generate facial landmarks of the 3D face model; [0048]: the processor 10 applies the set of face shape coefficients and the set of facial expression coefficients to the principal component analysis-based 3D morphable model 102 to obtain the 3D model point cloud to serve as the 3D prediction image).
Regarding to claim 9, Zhou in view of Abreu and Zhou (’359) discloses the method for generating a digital human facial expression according to claim 8 (same as rejected in claim 8), wherein the average reference mark is obtained by: (1) calculating initialized average reference marks (Zhou; [0016]: the 3D facial reconstruction system is adopted in 3D facial reconstruction, expression driving, and Avatar animation driving; employ the reconstructed face to obtain 3D landmark points, facial swapping, and face segmentation); (2) aligning the facial marks in the training set with the average reference marks and averaging the aligned facial marks to obtain updated average reference marks (Zhou; [0016]: the 3D morphable model is based on the principal component analysis (PCA), and adopts a plurality of model coefficients to generate facial landmarks of the 3D face model); iterating step (2) until the error between the facial marks in the training set and the average reference marks is within a tolerance range (Zhou; [0061]: an iterative closest point (ICP) algorithm is performed on the depth point cloud using the 3D inner points of a face database to initialize the pose; by repeating Steps S714 to S722 for several iterations, a more accurate 3D front angle image may be obtained).
Regarding to claim 10, Zhou discloses a method for generating a digital human facial expression model (Fig. 1; [0016]: use face shape coefficients to control a face shape of the 3D face model and use facial expression coefficients to control a facial expression of a 3D human face mode; enhance the accuracy of initial expression and individual expressions; [0048]: perform a forward propagation to generate a set of face shape coefficients and a set of facial expression coefficients; generate the face shape coefficients and expression coefficients of the 3D deformed model 102; [0071]: generate the 3D model of the user using the 3D morphable model 102 according to the set of corresponding face shape coefficients and the set of expression coefficients), comprising:
In same field of endeavor, Abreu teaches keyframes ([0129]: a sequence of images or video frames; the tracking module determines if an object, a subject's face in this exemplary embodiment, was previously detected and located for tracking in a prior image or video frame).
The rest of the claim limitations are similar to claim limitations recited in claim 1 and claim 2. Therefore, same rational used to reject claim 1 and claim 2 is also used to reject claim 10.
Regarding to claim 11, Zhou in view of Abreu and Zhou (’359) discloses the method for generating a digital human facial expression model according to claim 10,
The rest of the claim limitations are similar to claim limitations recited in claim 6. Therefore, same rational used to reject claim 6 is also used to reject claim 11.
Regarding to claim 12, Zhou in view of Abreu and Zhou (’359) discloses the method for generating a digital human facial expression model according to claim 10, wherein the step of determining the mapping relationship from the facial expressions to the 3D model based on the adjusted values of the controllers and coordinates of the annotated facial marks comprises using the following equation for fitting:
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where φ is the basis function, φ.sub.ji=φ(∥x.sub.j−x.sub.i∥), x represents the coordinates of the plurality of facial marks, N is the number of keyframes, and y represents the values of the controllers; a set of weights w is obtained for each keyframe by solving the above equation, and the weights for the plurality of keyframes are used as the mapping relationship from the human facial expression to the 3D model of the digital human (Zhou (‘359); Zhou (‘359); [0015]: determine the regression factor matrix by minimizing an error between the extent to which the second form coefficient is updated and an extent to which a third form coefficient is updated, calculated based on the nonlinear feature; [0016]: obtain a regression factor matrix R; [0018]: obtain a similarity transformation matrix by arranging an intermediate form and an average form obtained through an update; Fig. 2B; [0064]: as in the images 203 through 206, a form coefficient is iteratively updated until the form coefficient converges using Equation 4;
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; [0078-0079]:
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).
Regarding to claim 13, Zhou in view of Abreu and Zhou (’359) discloses the method for generating a digital human facial expression model according to claim 10,
The rest claim limitations are similar to claim limitations recited in claim 8. Therefore, same rational used to reject claim 8 is also used to reject claim 13.
Regarding to claim 14, Zhou in view of Abreu and Zhou (’359) discloses the method for generating a digital human facial expression model according to claim 13,
The rest claim limitations are similar to claim limitations recited in claim 9. Therefore, same rational used to reject claim 9 is also used to reject claim 14.
Regarding to claim 15, Zhou in view of Abreu and Zhou (’359) discloses the method for generating a digital human facial expression model according to claim 10,
The rest claim limitations are similar to claim limitations recited in claim 7. Therefore, same rational used to reject claim 7 is also used to reject claim 15.
Regarding to claim 16, Zhou in view of Abreu and Zhou (’359) discloses the method for generating a digital human facial expression model according to claim 10, wherein the value of each controller indicates the intensity of a specific expression of the digital human (Abreu; [0069]: an intensity is defined by the associated grey value; [0095]: the shape model parameters and the appearance model parameters are combined, with a weighting that measures the unit differences between shape (distances) and appearance, i.e. intensities; [0156]: the overall computed highlight intensity calculated by this exemplary lip shader module).
Same motivation of claim 10 is applied here.
Regarding to claim 17, Zhou discloses a device for generating digital human facial expressions (Fig. 1; [0016]: use face shape coefficients to control a face shape of the 3D face model and use facial expression coefficients to control a facial expression of a 3D human face mode; enhance the accuracy of initial expression and individual expressions; [0048]: perform a forward propagation to generate a set of face shape coefficients and a set of facial expression coefficients; generate the face shape coefficients and expression coefficients of the 3D deformed model 102; [0071]: generate the 3D model of the user using the 3D morphable model 102 according to the set of corresponding face shape coefficients and the set of expression coefficients), comprising:
at least one memory comprising instructions ([0004]: the memory is coupled to the processor and is used to store a plurality of instructions.); and
at least one processor coupled to the at least one memory and configured to ([0004]: the processor is used to execute the plurality of instructions to generate a 3D front angle image of a 3D ground truth model according to the main color image and the main depth map):
the rest claim limitations are similar to claim limitations recited in claim 1. Therefore, same rational used to reject claim 1 is also used to reject claim 17.
Regarding to claim 18, Zhou in view of Abreu and Zhou (’359) discloses the device for generating digital human facial expressions according to claim 17, wherein the at least one processor is configured to (same as rejected in claim 17)
The rest of the claim limitations are similar to claim limitations recited in claim 1. Therefore, same rational used to reject claim 1 is also used to reject claim 18.
Regarding to claim 19, Zhou in view of Abreu and Zhou (’359) discloses the device for generating digital human facial expressions according to claim 17,
The rest claim limitations are similar to claim limitations recited in claim 2. Therefore, same rational used to reject claim 2 is also used to reject claim 19.
Regarding to claim 20, Zhou in view of Abreu and Zhou (’359) discloses the device for generating digital human facial expressions according to claim 17, wherein the at least one processor is configured to (same as rejected in claim 17)
The rest of the claim limitations are similar to claim limitations recited in claim 3. Therefore, same rational used to reject claim 3 is also used to reject claim 20.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Hai Tao Sun whose telephone number is (571)272-5630. The examiner can normally be reached 9:00AM-6:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Daniel Hajnik can be reached at 5712727642. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HAI TAO SUN/Primary Examiner, Art Unit 2616
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